docs: def compute_laplacian_matrix(self, copy=True, return_lapsym=False, **kwargs):
          """
          Note: this function will compute the laplacian matrix. In order to acquire
              the existing laplacian matrix use self.laplacian_matrix as
              comptute_laplacian_matrix() will re-compute the laplacian matrix.
  
          Parameters
          ----------
          copy : boolean, whether to return copied version of the self.laplacian_matrix
          return_lapsym : boolean, if True returns additionally the symmetrized version of
              the requested laplacian and the re-normalization weights.
          **kwargs : see laplacian.py docmuentation for arguments for each method.
  
          Returns
          -------
          self.laplacian_matrix : sparse matrix (N_obs, N_obs).
              The requested laplacian.
          self.laplacian_symmetric : sparse matrix (N_obs, N_obs)
              The symmetric laplacian.
          self.laplacian_weights : ndarray (N_obs,)
              The renormalization weights used to make
              laplacian_matrix from laplacian_symmetric
          """
          if self.affinity_matrix is None:
              self.compute_affinity_matrix()
  
          kwds = self.laplacian_kwds.copy()
          kwds.update(kwargs)
          kwds['full_output'] = return_lapsym
          result = compute_laplacian_matrix(self.affinity_matrix,
                                            self.laplacian_method,
                                            **kwds)
          if return_lapsym:
              (self.laplacian_matrix,
               self.laplacian_symmetric,
               self.laplacian_weights) = result
          else:
              self.laplacian_matrix = result
  
          if copy:
              return self.laplacian_matrix.copy()
          else:
              return self.laplacian_matrix